Academy Awards Redux

By the time a new feature or data set is released for public consumption in Wolfram|Alpha, it has already been through a long process of analysis, curation, and design… but even after all of that, we still have our share of “D’oh!” moments at the eleventh hour.

Our latest forehead-smacker was pointed out to us just as we were about to announce the release of historical Academy Awards data. Fortunately, Wolfram|Alpha is flexible enough that we were able to implement a quick, partial fix before this year’s Oscars ceremony—but we also had to go back and do some more substantial work so this data is presented with absolute clarity.

So what was the problem? We had taken for granted the idea that when users typed in “2010 Academy Awards”, they’d expect to see people who won Oscars at this year’s ceremony… and then we just counted backward from there, to the first Oscars ceremony in 1929. But as it was pointed out, if you ask “who won the Oscar for best supporting actor in 2005”, you might want to know about films released in 2005, not films honored at the 2005 Academy Awards ceremony. So now when you ask for information about Oscars we assume you mean the year of the award ceremony, but for most years you can also click on a single link in the assumption pod to interpret your input as referring to year of film release instead.

We’ve also cleaned up the presentation of some quirks in Oscar history, including the unique case of the Academy Awards in 1930—when there were actually two ceremonies in a single year, one for films released in 1928–29, and one for films released in 1929–30:

Even though we got some extremely positive feedback on our initial release of Academy Awards data, we still thought it was important to go back and really do it right. Our ultimate goal is to have user inputs “just work”—or to have exactly the right answer be no more than a click away, if our initial assumption isn’t exactly what you had in mind. Are there other places in Wolfram|Alpha where you think we need a little more nuance in our input interpretations? Let us know in the comments below.